def main(posargs):
# Check then handle the positional arguements
# brough in from the command line
if len(posargs) < 3:
raise ValueError("At least three arguments are required.\n")
name = posargs.pop()
stat = posargs[0]
tocompare = posargs[1:]
# Create a csv file to tabulate into
f = open('{0}_{1}.csv'.format(name, stat), 'w')
csvw = csv.writer(f)
csvw.writerow(["mean", "sd", "D", "dataset", "model", "boldmeta",
"dmmeta", "cond"])
models = get_model_names(tocompare[0]) ## Assume models are identical for
## each hdf to compare
for model in models:
for ii, comp in enumerate(tocompare):
meta = get_model_meta(comp, model)
boldmeta = "_".join([str(b) for b in meta["bold"]])
dmmeta = "_".join([str(d) for d in meta["dm"]])
# Generate the data to add to the table.
hist_list = create_hist_list(comp, model, stat)
means = [hist.mean() for hist in hist_list]
stdevs = [hist.stdev() for hist in hist_list]
names = [hist.name for hist in hist_list]
# Calculate effect sizes
ns = [hist.n() for hist in hist_list]
k = len(hist_list) + 1 ## Number of predcitors
## +1 for the dummy
cohen_ds = [(2.0 * mean) / np.sqrt(n - k - 1.0) for
mean, n in zip(means, ns)]
## d = 2*t / sqrt(DF)
## DF = n - k - 1
## n = sample number
## k = predictor number
# And add it.
databasename = os.path.splitext(os.path.basename(comp))[0]
for mean, sd, d, name in zip(means, stdevs, cohen_ds, names):
row = [mean, sd, d, databasename, model, boldmeta, dmmeta, name]
csvw.writerow(row)
f.close()
def main(posargs):
# Check then handle the positional arguements
# brough in from the command line
if len(posargs) != 4:
raise ValueError("Four arguments are required.\n")
hdf = posargs[0]
stat = posargs[1]
criterion = float(posargs[2])
name = posargs[3]
# Create csv writer named name
# And give it a header
f = open("{0}_{1}{2}.csv".format(name, stat, criterion), "w")
csvw = csv.writer(f)
csvw.writerow(["area", "model", "boldmeta", "dmmeta", "cond"])
models = get_model_names(hdf)
for model in models:
hist_list = create_hist_list(hdf, model, stat)
# Loop over the hist_list adding
# the results from each hist.above(criterion)
# to a bar plot.
areas = [hist.above(criterion) for hist in hist_list]
names = [hist.name for hist in hist_list]
# Pretty things up then save
# this barplot to the pdf
# and move onto the next model
meta = get_model_meta(hdf, model)
boldmeta = "_".join([str(b) for b in meta["bold"]])
dmmeta = "_".join([str(d) for d in meta["dm"]])
for area, name in zip(areas, names):
row = [area, model, boldmeta, dmmeta, name]
csvw.writerow(row)
f.close()
